Intern(Chow Jun Hoe)

 15/8/2025 Building on the optimization of the rolling forecast model, I added cosine and sinine transformations based on periodicity to handle date information in time series. Verification results show that this method maintains forecast consistency across year-ends (especially for dates like 1/1/2025), effectively avoiding forecast instability caused by date code jumps.

 14/8/2025 Today, I successfully converted my XGBoost model to a rolling forecast mode, achieving a significant reduction in the MAPE. This demonstrates that this step-by-step forecasting approach is indeed helpful in improving overall forecast accuracy. However, the rolling forecast mode fails to effectively account for some holiday factors, particularly sales fluctuations near holiday times. Further research is needed to identify solutions.

 13/8/2025 Today's main task is to convert the XGBoost model to a rolling forecast mode, so that the step-by-step forecasting process is more closely aligned with real-world business needs. This adjustment not only involves modifying the structure of the forecasting process but also replanning the split of training and test data to ensure that each forecast step is updated based on known historical data.

 12/8/2025 In the morning, I mainly assisted with the transportation of supplies for the charity event, loading the relevant goods onto the truck in an orderly manner. In the afternoon, I focused on optimizing the XGBoost sales forecasting model. This included checking feature importance, adjusting parameter combinations, and experimenting with introducing new data features to improve the accuracy and stability of the forecast.

 11/8/2025 Today’s main task is to continue adjusting Coded OOS, and after completion, continue to study XGBoost model optimization.

 8/8/2025 My primary task today was to present the previously simplified Coded Out-of-Stock (OOS) information to the relevant teams and engage in in-depth discussions with them to evaluate whether the simplified version meets business needs and actual application scenarios. During the discussion, everyone provided feedback and raised questions regarding the feasibility, accuracy, and compatibility of the simplified logic with existing processes. After analysis and discussion, I discovered that the current simplified solution, in some cases, failed to fully reflect the true cause of the out-of-stock situation, potentially leading to biased subsequent data analysis and decision-making. Therefore, we ultimately decided to restructure and restructure the version to ensure it remains concise while still meeting the business department's usage standards and accuracy requirements.